SPT-Based Probabilistic Method for Evaluation of Liquefaction Potential of Soil Using Multi-Gene Genetic Programming

2013 ◽  
Vol 4 (1) ◽  
pp. 42-60 ◽  
Author(s):  
Pradyut Kumar Muduli ◽  
Sarat Kumar Das
Keyword(s):  
Genetic Programming ◽  
Probabilistic Method ◽  
Limit State ◽  
Mapping Function ◽  
Standard Penetration Test ◽  
State Function ◽  
Liquefaction Potential ◽  
Successful Prediction ◽  
Bayesian Mapping ◽  
Artificial Neural Network Ann

The present study discusses about evaluation of liquefaction potential of soil within a probabilistic framework based on the standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). Based on the developed limit state function, a relationship is given between probability of liquefaction and factor of safety against liquefaction using Bayesian theory. This Bayesian mapping function is further used to develop a probabiliy based design chart for evaluation of liquefaction potential of soil. Using an independent database the efficacy of present MGGP based probabilistic model is compared with the available artificial neural network (ANN) and statistical models in terms of rate of successful prediction of liquefaction and non-liquefaction cases. The proposed MGGP based model is found to be more accurate compared to other models.

A new genetic programming model for predicting settlement of shallow foundations

2007 ◽  
Vol 44 (12) ◽  
pp. 1462-1473 ◽  
Author(s):  
Mohammad Rezania ◽  
Akbar A. Javadi
Keyword(s):  
Genetic Programming ◽  
Programming Model ◽  
Standard Penetration Test ◽  
Shallow Foundations ◽  
Penetration Test ◽  
Case Histories ◽  
Contributing Factors ◽  
Artificial Neural Network Ann ◽  
Gp Model ◽  
Very High

In this paper, a new genetic programming (GP) approach for predicting settlement of shallow foundations is presented. The GP model is developed and verified using a large database of standard penetration test (SPT) based case histories that involve measured settlements of shallow foundations. The results of the developed GP model are compared with those of a number of commonly used traditional methods and artificial neural network (ANN) based models. It is shown that the GP model is able to learn, with a very high accuracy, the complex relationship between foundation settlement and its contributing factors, and render this knowledge in the form of a function. The attained function can be used to generalize the learning and apply it to predict settlement of foundations for new cases not used in the development of the model. The advantages of the proposed GP model over the conventional and ANN based models are highlighted.

Risk-based liquefaction potential evaluation using standard penetration tests

2000 ◽  
Vol 37 (6) ◽  
pp. 1195-1208 ◽  
Author(s):  
C Hsein Juang ◽  
Caroline J Chen ◽  
Tao Jiang ◽  
Ronald D Andrus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Liquefaction Potential ◽  
New Approach ◽  
Mapping Functions ◽  
Artificial Neural ◽  
Penetration Tests

In this paper, a new approach is presented for developing a liquefaction limit state function, which defines a boundary that separates liquefaction from no-liquefaction occurrence. The new approach is developed using a database consisting of 243 field liquefaction performance cases at sites where standard penetration tests (SPT) had been conducted. This database is first used to train and test an artificial neural network for predicting the occurrence of liquefaction or no liquefaction. The successfully trained neural network is then used to establish a liquefaction limit state function. Based on the developed limit state function, mapping functions that relate calculated factors of safety to probability of liquefaction are established. The established mapping functions form a basis for the development of a risk-based chart for liquefaction potential evaluation.Key words: probability, risk-based design, liquefaction potential, SPT, artificial neural network.

Effects of Various Types of Distribution on Probabilistic Method and FAD

2007 ◽  
Vol 353-358 ◽  
pp. 2561-2564
Author(s):  
Ouk Sub Lee ◽  
Dong Hyeok Kim
Keyword(s):  
Failure Probability ◽  
Probabilistic Method ◽  
Limit State ◽  
Random Variables ◽  
Reliability Estimation ◽  
Probabilistic Methods ◽  
State Function ◽  
Operating Pressure ◽  
Lognormal Distributions ◽  
Reliability Method

The reliability estimation of pipeline is performed in accordance with the probabilistic methods such as the FORM (first order reliability method) and the SORM (second order reliability method). A limit state function has been formulated with help of the FAD (failure assessment diagram). Various types of distribution of random variables are assumed to investigate its effect on the failure probability. It is noted that the failure probability increases with the increase of the dent depth, the operating pressure and the outside radius, and the decrease of the wall thickness. Furthermore it is found that the failure probability for the random variables having the Weibull distribution is larger than those of the normal and the lognormal distributions.

DOUBLE-HINGED ARTICULATED OFFSHORE TOWER UNDER SEISMIC EXCITATION

2010 ◽  
Vol 04 (03) ◽  
pp. 197-214
Author(s):  
NAZRUL ISLAM ◽  
SYED DANISH HASAN ◽  
SUHAIL AHMAD
Keyword(s):  
Probabilistic Method ◽  
Limit State ◽  
Equations Of Motion ◽  
Earthquake Ground Motion ◽  
Integration Scheme ◽  
State Function ◽  
Limit State Function ◽  
Sea Waves ◽  
Seismic Demands ◽  
Ground Motion Records

The seismic response and reliability analysis of a double-hinged articulated offshore tower under the action of sea waves, currents, and earthquakes have been investigated. Major nonlinearities associated with the system such as large deformation, variable submergence, drag force, and added mass have been incorporated. The responses are obtained by spectral analyses using El Centro earthquake ground motion records. The equations of motion are formulated by Lagrangian approach and solved by Newmark β integration scheme. A limit-state function for seismic demands at the universal joint has been derived. Using the derived limit-state function and the responses obtained after the dynamic analysis, reliability assessment of the critical joint has been carried out with a probabilistic method. The results of the study show that the earthquake response investigations are quite crucial for target-based probabilistic design of the articulated system, as seismic sea environment imposes significant demands for the survival of the tower.

A non-probabilistic time-variant method for structural reliability analysis

2020 ◽  
Vol 234 (5) ◽  
pp. 664-675
Author(s):  
Tengda Xin ◽  
Jiguang Zhao ◽  
Cunyan Cui ◽  
Yongsheng Duan
Keyword(s):  
Reliability Analysis ◽  
Reliability Index ◽  
Structural Reliability ◽  
Probabilistic Method ◽  
Limit State ◽  
Interference Method ◽  
Index Formula ◽  
State Function ◽  
Interval Method ◽  
Variant Method

Time-variant reliability problems commonly occur in practical engineering due to the deterioration in material properties, external disturbance and other uncertain factors. Considering the non-probabilistic method can effectively deal with the uncertainties in reliability analysis. Based on the stress–strength interference method and interval method, a time-variant stress–strength interference interval model is established by considering the stress and strength as time-variant intervals. And then, the stress and strength intervals are converted into the normalized intervals to define the non-probabilistic time-variant reliability index [Formula: see text] according to the different relationships between the limit state function and the normalized intervals. The structural state at any time can be described by the non-probabilistic time-variant reliability index [Formula: see text]. In addition, a strength power exponential degradation model is given as an example to clearly verify the non-probabilistic time-variant method, and the analysis results are compared with the interval method, the uniform distribution stress–strength interference method and the normal distribution stress–strength interference method, which confirm that the non-probabilistic time-variant method is feasible and valid to analyze the structural time-variant reliability without the probability density functions of the parameters.

Reliability and reliability-based sensitivity analysis of self-centering buckling restrained braces using meta-models

2021 ◽  
pp. 1045389X2110263
Author(s):  
Seyede Vahide Hashemi ◽  
Mahmoud Miri ◽  
Mohsen Rashki ◽  
Sadegh Etedali
Keyword(s):  
Failure Probability ◽  
Limit State ◽  
Computational Cost ◽  
Sensitivity Analyses ◽  
State Function ◽  
Reliability Indices ◽  
Buckling Restrained Brace ◽  
Polynomial Response Surface ◽  
Nonlinear Dynamic Analyses ◽  
High Computational Cost

This paper aims to carry out sensitivity analyses to study how the effect of each design variable on the performance of self-centering buckling restrained brace (SC-BRB) and the corresponding buckling restrained brace (BRB) without shape memory alloy (SMA) rods. Furthermore, the reliability analyses of BRB and SC-BRB are performed in this study. Considering the high computational cost of the simulation methods, three Meta-models including the Kriging, radial basis function (RBF), and polynomial response surface (PRSM) are utilized to construct the surrogate models. For this aim, the nonlinear dynamic analyses are conducted on both BRB and SC-BRB by using OpenSees software. The results showed that the SMA area, SMA length ratio, and BRB core area have the most effect on the failure probability of SC-BRB. It is concluded that Kriging-based Monte Carlo Simulation (MCS) gives the best performance to estimate the limit state function (LSF) of BRB and SC-BRB in the reliability analysis procedures. Considering the effects of changing the maximum cyclic loading on the failure probability computation and comparison of the failure probability for different LSFs, it is also found that the reliability indices of SC-BRB were always higher than the corresponding reliability indices determined for BRB which confirms the performance superiority of SC-BRB than BRB.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

An Efficient Response Surface Method Using Givens Transformation for Structural Reliability Analysis

2012 ◽  
Vol 532-533 ◽  
pp. 408-411
Author(s):  
Wei Tao Zhao ◽  
Yi Yang ◽  
Tian Jun Yu
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Structural Reliability ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Surface Method ◽  
Mathematical Techniques ◽  
Input Variables ◽  
Improved Accuracy

The response surface method was proposed as a collection of statistical and mathematical techniques that are useful for modeling and analyzing a system which is influenced by several input variables. This method gives an explicit approximation of the implicit limit state function of the structure through a number of deterministic structural analyses. However, the position of the experimental points is very important to improve the accuracy of the evaluation of failure probability. In the paper, the experimental points are obtained by using Givens transformation in such way these experimental points nearly close to limit state function. A Numerical example is presented to demonstrate the improved accuracy and computational efficiency of the proposed method compared to the classical response surface method. As seen from the result of the example, the proposed method leads to a better approximation of the limit state function over a large region of the design space, and the number of experimental points using the proposed method is less than that of classical response surface method.

A Reliability-Based Early Warning System for Real-Time Monitoring on RC Buildings

2012 ◽  
Vol 446-449 ◽  
pp. 3422-3427
Author(s):  
Wang Sheng Liu ◽  
Ming Zhao
Keyword(s):  
Probability Distribution ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Limit State ◽  
Warning System ◽  
State Function ◽  
Real Time Monitoring ◽  
Rc Buildings ◽  
Load Effect

Today there is an urgent need for effective monitoring whether for old buildings or new ones. While conventional early warning system for real-time monitoring is based on safety factor, this paper proposes a new reliability-based framework to monitor the safety of RC buildings probabilistically. The framework includes modeling resistance, predicting probability distribution of load effect, calculating reliability and setting reliability index threshold. The in-situ test data enables to update the resistance model through a Bayesian process. Meanwhile, the observed monitoring data predicts the probability distribution of load effect. FORM is used to calculate the reliability because the limit state function for real-time monitoring is linear and simple. This study shows that the reliability-based early warning system is of more scientific sense in quantifying the safety and may be applied to many engineering fields.

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